Imaging Detector Datasets Amir Farbin
Frontiers • Energy Frontier : Large Hadron Collider (LHC) at 13 TeV now, High Luminosity (HL)- LHC by 2025, perhaps 33 TeV LHC or 100 TeV Chinese machine in a couple of decades. • Having found Higgs, moving to studying the SM Higgs find new Higgses • Test naturalness (Was the Universe and accident?) by searching for New Physics 18 like Supersymmetry that keeps Higgs light without 1 part in 10 fine-tuning of parameters. • Find Dark Matter (reasons to think related to naturalness) • Intensity Frontier : • B Factories : upcoming SuperKEKB/SuperBelle • Neutrino Beam Experiments : • Series of current and upcoming experiments: Nova, MicroBooNE, SBND, ICURUS • US’s flagship experiment in next decade: Long Baseline Neutrino Facility (LBNF)/Deep Underground Neutrino Experiment (DUNE) at Intensity Frontier • Measure properties of b-quarks and neutrinos (newly discovered mass)… search for matter/anti-matter asymmetry . e+ bunch Damping Rings IR & detectors compressor e- source • Auxiliary Physics: Study Supernova . Search for Proton Decay and Dark Matter . e+ source e- bunch positron compressor 2 km • Precision Frontier : International Linear Collider (ILC) , hopefully in next decade. Most main linac + - 11 km energetic e e machine. central region 5 km electron • Precision studies of Higgs and hopefully new particles found at LHC. main linac 11 km 2 km
Where is ML needed? • Traditionally ML Techniques in HEP • Applied to Particle/Object Identification • Signal/Background separation • Here, ML maximizes reach of existing data/detector… equivalent to additional integral luminosity. • There is lots of interesting work here… and potential for big impact. • Now we hope ML can help address looming computing problems • Reconstruction • LArTPC- Algorithmic Approach very difficult • HL-LHC Tracking- Pattern Recognition blows up due to combinatorics • Simulation • LHC Calorimetry- Large Fraction of ATLAS CPU goes into shower simulation.
LArTPC Reco Challenge Neutrino Physics has a long history of hand scans . • QScan: ICARUS user assisted reconstruction. • ! Full automatic reconstruction has yet to be • demonstrated. ! LArSoft project: • ! art framework + LArTPC reconstruction • algorithm started in ArgoNeuT and contributed to/used • by many experiments. Full neutrino reconstruction is still far from • expected performance. ICARUS_2015 Slide# : 9
Computing Challenge • Computing is perhaps the biggest challenge for the HL-LHC • Higher Granularity = larger events. • O(200) proton collision / crossing: tracking pattern recognition combinatorics becomes untenable . • O(100) times data = multi exabyte datasets . • Moore’s law has stalled : Cost of adding more transistors/silicon area no longer decreasing…. for processors. Many-core co-processors still ok. • Naively we need 60x more CPU, with 20%/year Moore’s law giving only 6-10x in 10-11 years. • Preliminary estimates of HL-LHC computing budget many times larger than LHC . • Solutions : • Leverage opportunistic resources and HPC (most computation power in highly parallel processors). • Highly parallel processors (e.g. GPUs) are already > 10x CPUs for certain computations. • Trend is away from x86 towards specialized hardware (e.g. GPUs, Mics, FPGAs, Custom DL Chips) • Unfortunately parallelization (i.e. Multi-core/GPU) has been extremely difficult for HEP. From WLCG Workshop Intro, Ian Bird, 8 Oct, 2016
Reconstruction
How do we “see” particles? • Charged particles ionize media • Image the ions. • In Magnetic Field the curvature of trajectory measures momentum . • Momentum resolution degrades as less curvature: σ (p) ~ c p ⊕ d. • d due to multiple scattering. • Measure Energy Loss (~ # ions) • dE/dx = Energy Loss / Unit Length = f(m, v) = Bethe-Block Function • Identify the particle type • Stochastic process (Laudau) • Loose all energy → range out. • Range characteristic of particle type.
Tracking • Measure Charged particle trajectories. If B-field, then measure momentum.
How do we “see” particles? • Particles deposit their energy in a stochastic process know as “showering” , secondary particles, that in turn also shower. • Number of secondary particles ~ Energy of initial particle. • Energy resolution improves with energy: σ (E) / E = a/ √ E ⊕ b/E ⊕ c. • a = sampling, b = noise, c = leakage. X 0 • Density and Shape of shower characteristic of type of particle. • Electromagnetic calorimeter : Low Z medium 0 → γγ interact with electrons • Light particles : electrons, photons, π in medium • Hadronic calorimeters : High Z medium • Heavy particles : Hadrons (particles with quarks, e.g. charged pions/protons, neutrons, or jets of such particles) • Punch through low Z. • Produce secondaries through strong interactions with the nucleus in medium. • Unlike EM interactions, not all energy is observed.
Calorimetry Make particle interact and loose all energy, which we measure. 2 types: • Electromagnetic: e.g. crystals in CMS, Liquid Argon in ATLAS. • Hadronic: e.g. steel + • scintillators e.g ATLAS: • 200K Calorimeter cells • measure energy deposits. 64 x 36 x 7 3D Image •
LHC/ILC detectors
Neutrino Detection In neutrino In ne no e experime ment nts, t , try t y to d determi mine ne f fla lavor a and nd e estima mate e ene nergy o y of inc ncomi ming ng ne neutrino no b by lo y looki king ng a at o outgoing ng p products o of t the he i int nteraction. n. Typical neutrino event ! Outgoing ng le lepton: n: Flavor: CC vs. NC, ! + vs. ! - , e vs. " Incomi Inc ming ng ne neutrino no: : Energy: measure Flavor unknown Energy unknown Mesons ns: : Final State Interactions Energy? Identity? Target nu nucle leus: : Outgoing ng nu nucle leons ns: : Nucleus remains intact for low Q 2 Visible? Energy? N-N correlations Jen Raaf
Neutrino Detectors • Need large mass/volume to maximize chance of neutrino interaction. • Technologies: • Water/Oil Cherenkov • Segmented Scintillators • Liquid Argon Time Projection Chamber: promises ~ 2x detection efficiency. • Provides tracking, calorimetry, and ID all in same detector . • Chosen technology for US’s flagship LBNF/DUNE program. • Usually 2D read-out… 3D inferred. • Gas TPC: full 3D ArgoNeuT ν e -CC candidate 2 π 0 ’s 10
HEP Computing Full Simulation Fast Simulation KHz KHz Generation Generation mHz Hz Simulation Fast Simulation Hz Digitization Hz Reconstruction KHz High-level Trigger Hz Derivation 1000 Hz Statistical Analysis Data Analysis & 10 9 events/year Calibration
Reconstruction EventSelector Service • Starts with raw inputs (e.g. (a) Voltages) Cell Channels • Low level Feature Extraction : e,g, Builder Energy/Time in each Calo Cell Cells Cell • Pattern Recognition : Cluster adjacent Cell Correction A cells. Find hit pattern. Cell Calibrator Correction B • Fitting : Fit tracks to hits. Cells Cluster • Combined reco : e.g.: Builder e r • Matching Track+EM Cluster = Electron. o Clusters t Cluster S Cluster • Matching Track in inter detector + Correction A a t Calibrator a Cluster D muon system = Muon Correction B Clusters t n • Output particle candidates and e Noise Cutter i s Jet Finder n measurements of their properties (e.g. a Jet Finder r T Jets energy) Jet Correction
Deep Learning
Why go Deep? • Better Algorithms • DNN-based classification/regression generally out perform hand crafted algorithms. • In some cases, it may provide a solution where algorithm approach doesn’t exist or fails . • Unsupervised learning : make sense of complicated data that we don’t understand or expect. • Easier Algorithm Development : Feature Learning instead of Feature Engineering • Reduce time physicists spend writing developing algorithms, saving time and cost . (e.g. ATLAS > $250M spent software) • Quickly perform performance optimization or systematic studies . • Faster Algorithms • After training, DNN inference is often faster than sophisticated algorithmic approach. • DNN can encapsulate expensive computations , e.g. Matrix Element Method. • Generative Models enable fast simulations. • Already parallelized and optimized for GPUs/HPCs. • Neuromorphic processors. 17
Datasets
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